Photo: Getty Images / Justin Sullivan
The sands are shifting under tech industry heavyweights like Facebook and Google.Users are spending more time with their smartphones and tablets and less time on their desktops and laptops.
This transition is happening so fast that the business models which support the industry are struggling to catch up. The evidence can be seen in Google’s Q3 earnings, where lower mobile ad rates deflated overall CPCs by 15 per cent.
At the heart of the problem is a fundamental change in how we use our computing devices. The experience most users have when they are at their desk on their laptop is typically far more active than when they are using their tablet on the couch or their smartphone on the bus. Users are far more likely to plan a vacation or do their holiday shopping on their desktop or laptop than on their smartphone or tablet. As a result, advertisers and merchants have been reluctant to expand their marketing budgets to reach these new post-PC users.
In just a few years, a large percentage of consumers may no longer even own a traditional PC or laptop. This simple fact is sounding the general alarm inside ad-driven businesses like Google, Facebook and Twitter. While this is a frightening prospect for many industry heavyweights, it also represents an enormous opportunity for the companies who manage to crack the code on post-PC advertising.
So, what’s the solution?
How do we bring the revenue potential for post-PC apps to parity with their cousins on the desktop and laptop? The first step is to understand the nature of the problem. Today, when users want to research products or make a transaction, they are more likely to put down their smartphone or tablet and open their laptop.
The reason for this is simple: performing detailed work on mobile and touch devices can be cumbersome. Not only is the screen real estate limited, but simple tasks like copy and paste, keyboard typing, app switching and web browsing are more laborious on touch devices than on desktops or laptops with a keyboard and mouse. As a result, users are habitually more passive and less interactive when using their post-PC devices.
We need better front-end apps and better back-end software to facilitate streamlined interaction on post-PC devices. The good news is that this is entirely possible, and it can be achieved by combining machine intelligence and predictive analytics with the abundance of contextual data available from post-PC devices.
Imagine you are having a conversation on your smartphone and your friend suggests that you watch the new James Bond movie tonight. Today, if you are on-the-go, you would probably wait until you reached your home or office before you got online and bought tickets. A couple years from now, I suspect that your phone, having understood your conversation and knowing your location, will automatically give you the option to purchase tickets at a local theatre in one or two taps as soon as your call ends.
While the engineering required to realise an example like this is not trivial, it is most certainly achievable through clever application of technologies available today.
Specifically, applications will need to continuously analyse and better understand a variety of input data signals such as location, audio, and online activity streams. Second, applications will need better models in order to glean insights and make targeted recommendations based on this abundance of contextual data. Last, applications will need to perform proactive, real-time search and data gathering behind the scenes to intelligently narrow down all the available options to just the few we need.
With capabilities like these, it is not hard to image how the monetization potential of post-PC applications can far surpass the revenue models supporting companies like Google and Facebook today. Given the coming explosion in the number and variety of computing devices in our lives, this represents a huge opportunity for the organisations that can crack the code on this new generation of intelligent applications.
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